Foundations for Near-Term Quantum Natural Language Processing

Abstract

We provide conceptual and mathematical foundations for near-term quantum natural language processing (QNLP), and do so in quantum computer scientist friendly terms. We opted for an expository presentation style, and provide references for supporting empirical evidence and formal statements concerning mathematical generality. We recall how the quantum model for natural language that we employ canonically combines linguistic meanings with rich linguistic structure, most notably grammar. In particular, the fact that it takes a quantum-like model to combine meaning and structure, establishes QNLP as quantum-native, on par with simulation of quantum systems. Moreover, the now leading Noisy Intermediate-Scale Quantum (NISQ) paradigm for encoding classical data on quantum hardware, variational quantum circuits, makes NISQ exceptionally QNLP-friendly: linguistic structure can be encoded as a free lunch, in contrast to the apparently exponentially expensive classical encoding of grammar. Quantum speed-up for QNLP tasks has already been established in previous work with Will Zeng. Here we provide a broader range of tasks which all enjoy the same advantage. Diagrammatic reasoning is at the heart of QNLP. Firstly, the quantum model interprets language as quantum processes via the diagrammatic formalism of categorical quantum mechanics. Secondly, these diagrams are via ZX-calculus translated into quantum circuits. Parameterisations of meanings then become the circuit variables to be learned. Our encoding of linguistic structure within quantum circuits also embodies a novel approach for establishing word-meanings that goes beyond the current standards in mainstream AI, by placing linguistic structure at the heart of Wittgenstein's meaning-is-context.

43 pages, lots of pictures

References in corpus (23)

• Experimental quantum teleportation
• Language Models are Few-Shot Learners
• Quantum Teleportation is a Universal Computational Primitive
• Quantum random access memory
• Resource-efficient linear optical quantum computation
• Towards a Formulation of Quantum Theory as a Causally Neutral Theory of Bayesian Inference
• Experimental Support for a Categorical Compositional Distributional Model of Meaning
• Quantum common causes and quantum causal models
• Reducing T-count with the ZX-calculus
• The Frobenius anatomy of word meanings I: subject and object relative pronouns
• On the Quantum versus Classical Learnability of Discrete Distributions
• Quantum Algorithms for Compositional Natural Language Processing
• Kindergarten Quantum Mechanics
• A Study of Entanglement in a Categorical Framework of Natural Language
• Teleportation Topology
• Picturing classical and quantum Bayesian inference
• The Frobenius anatomy of word meanings II: possessive relative pronouns
• DisCoPy: Monoidal Categories in Python
• Compositional Distributional Semantics with Compact Closed Categories and Frobenius Algebras
• Quantum Language Processing
• A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure
• Qutrit Dichromatic Calculus and Its Universality
• Meaning updating of density matrices

Cited by in corpus (9)

• QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer
• Quantum Machine Learning: from physics to software engineering
• ZX-calculus for the working quantum computer scientist
• Quantum Mathematics in Artificial Intelligence
• Conversational Negation using Worldly Context in Compositional Distributional Semantics
• Kindergarden quantum mechanics graduates (...or how I learned to stop gluing LEGO together and love the ZX-calculus)
• Fast Extraction of Word Embedding from Q-contexts
• Functorial Language Models
• Grover's Algorithm for Question Answering